WATT FOR WHAT: RETHINKING DEEP LEARNING’S ENERGY-PERFORMANCE RELATIONSHIP
Keywords: Energy tracking, Efficient learning, Metric, Power Consumption
TL;DR: We propose a metric to incorporate electricity consumed by a model into the evaluation in order to close the gap between users with large resources and ones with smaller resources.
Abstract: Deep learning models have revolutionized various fields, from image recognition to natural language processing, by achieving unprecedented levels of accuracy. However, their increasing energy consumption has raised concerns about their environmental impact, disadvantaging smaller entities in research and exacerbating global energy consumption. In this paper, we explore the trade-off between model accuracy and electricity consumption, proposing a metric that penalizes large consumption of electricity. We conduct a comprehensive study on the electricity consumption of various deep learning models across different GPUs, presenting a detailed analysis of their accuracy-efficiency trade-offs. By evaluating accuracy per unit of electricity consumed, we demonstrate how smaller, more energy-efficient models can significantly expedite research while mitigating environmental concerns. Our results highlight the potential for a more sustainable approach to deep learning, emphasizing the importance of optimizing models for efficiency. This research also contributes to a more equitable research landscape, where smaller entities can compete effectively with larger counterparts. This advocates for the adoption of efficient deep learning practices to reduce electricity consumption, safeguarding the environment for future generations whilst also helping ensure a fairer competitive landscape.
Primary Area: societal considerations including fairness, safety, privacy
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Submission Number: 3953
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